Seasonal land cover and annual land use mapping for fire modeling

2020 ◽  
Author(s):  
Peng Gong ◽  
Han Liu ◽  
Yuqi Bai
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Computational Cost ◽  
Fire Modeling ◽  
Machine Learning Algorithms ◽  
Data Cube ◽  
Fuel Type ◽  
Burnt Area ◽  
Type Mapping ◽  
Type Data

<p>Fire modeling needs timely fuel information.  Land cover and land use data are often used for fuel type mapping.  Existing large scale mapping efforts do not provide frequent land cover information, due partly to the lack of frequent raw data, and partly to the huge computational cost.  In this presentation, we will report our latest land cover and land use mapping efforts toward mapping global land cover at seasonal steps while mapping land use at annual intervals.  We report a data-cube approach applied to over 20-year Landsat and Terra and Aqua data (2000-2019) that made it convenient to experiment with various land cover and land use mapping procedures.  </p><p>With a data cube, time series analysis can be easily done that allows not only fuel type mapping but also fire event detection.  We report the use of multiple season land cover samples collected in a specific year at the global scale to map seasonal land cover.  We also report the use of historical land use for annual land use mapping. In addition, we report burnt area detection results from the using selected data from historical burnt area maps in training machine learning algorithms based on the data cube.  Land cover and land use data are cross-walked to fuel type data. This approach provide more accurate fuel type data for fire emission estimation and fire behavior modeling.</p><p> </p>

scholarly journals Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

2020 ◽  
Vol 12 (7) ◽  
pp. 1135 ◽  
Author(s):  
Swapan Talukdar ◽  
Pankaj Singha ◽  
Susanta Mahato ◽  
Shahfahad ◽  
Swades Pal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Learning Algorithms ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Land Use Land Cover ◽  
Accuracy Level ◽  
Parametric Classifier

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

scholarly journals Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

2020 ◽  
Vol 12 (18) ◽  
pp. 3062 ◽  
Author(s):  
Michel E. D. Chaves ◽  
Michelle C. A. Picoli ◽  
Ieda D. Sanches
Keyword(s):  
Systematic Review ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Near Infrared ◽  
Data Cube ◽  
Optical Data ◽  
Landsat 8 ◽  
Detailed Knowledge ◽  
Sentinel 2

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

scholarly journals Global assessment of land cover changes and rural-urban interface in Portugal

2017 ◽  
Author(s):  
Marj Tonini ◽  
Joana Parente ◽  
Mario Pereira
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Global Assessment ◽  
Fire Regime ◽  
Land Use Changes ◽  
Population Increase ◽  
Burnt Area ◽  
Agricultural Areas ◽  
The Impact

Abstract. The wildland-/rural-urban interface (WUI/RUI) is a particularly important aspect of the fire regime. In Mediterranean basin most of the fires in this pyro region are caused by humans and the risk and consequences are particularly high due to the close proximity to population, human infrastructures and urban areas. Population increase, urban growth and the rapid changes in land use incurred in Europe over the last 30 years has been unprecedented, especially nearby the metropolitan areas, and some of these trends are expected to continue. Associated to high socioeconomic development, Portugal experienced in the last decades significant land cover/land use changes (LCLUC), population dynamics and demographic trends in response to migration, rural abandonment, and ageing of rural population. This study aims to assess the evolution of RUI in Portugal, from 1990 to 2012, based on LCLUC providing also a quantitative characterization of forest fires dynamics in relation to the burnt area. Obtained results disclose important LCLUC which spatial distribution is far from uniform within the territory. A significant increase in artificial surfaces is registered nearby the main metropolitan communities of the northwest and littoral-central and southern regions, whilst the abandonment of agricultural land nearby the inland urban areas leads to an increase of uncultivated semi-natural and forest areas. Within agricultural areas, heterogeneous patches suffered the greatest changes and are the main contributors to the increase of urban areas. Moreover these are among the LCLU classes with higher burnt area, reasons why heterogeneous agricultural areas have been included in the definition of RUI. Finally, the mapped RUI’s area, burnt area and burnt area within RUI allow to conclude that, form 1990 to 2012 in Portugal, RUI increased more than two thirds and total burnt area decreased one third. Nevertheless, burnt area within RUI doubled, which emphasize the significance of RUI for land and fire managers. This research provides a first quantitative global assessment of RUI in Portugal and presents an innovative analysis on the impact of land use changes on burnt areas.

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